Discussion: Factorial Treatment Structure

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Discussion: Factorial Treatment Structure

Statistics is the science of collecting and analyzing data. It is often used in health sciences to help researchers understand relationships between different variables, such as risk factors and disease outcomes. Statistics can also be used to design clinical trials, assess the effectiveness of treatments, and predict future health trends (Tambe & Gajre et al., 2018). Because of its broad applications, statistics is an important tool for anyone working in the health sciences field.

Interestingly, the field of statistics has a long and rich history in the field of healthcare. In fact, many famous statisticians have made major contributions to the advancement of healthcare. For example, in the early 1800s, Dr. John Snow used statistical techniques to investigate the correlation between cholera outbreaks and contaminated water sources (Cho & Jeong, 2019). His work led to improved sanitation practices and helped save countless lives. More recently, statistics has been used to develop vaccination schedules and track disease outbreaks. It has also been instrumental in designing clinical trials and developing new treatments for various diseases. In short, statistics plays a vital role in ensuring that we provide the best possible care for our patients.

There are many examples of how statistical methods have been applied to healthcare in order to improve operations and patient outcomes. One well-known example is the use of Six Sigma techniques, introduced by Bill Smith, to reduce variation and errors in health care processes. Six Sigma is a set of statistical methods that are used to identify and eliminate sources of error and variation in order to improve quality and efficiency. It has been widely used in many industries, but it has been particularly successful in healthcare. Six Sigma has been used to reduce surgical infection rates, length of stay for hospital patients, waiting times for outpatient procedures, and many other aspects of healthcare delivery.

References

Cho, Y. S., & Jeong, S. P. (2019). Application of Disease Pattern Analysis with Lifestyle Risk Factor for Healthcare Promotion Service. 10.20944/preprints201904.0095.v2

Tambe, S. B., & Gajre, S. S. (2018). Cluster-based real-time analysis of mobile healthcare application for prediction of physiological data. Journal of Ambient Intelligence and Humanized Computing, 9(2), 429-445. https://link.springer.com/article/10.1007/s12652-017-0562-9

 

Question Description
Can you help me understand this Statistics question?

Please use R codes for all the problems except 8.2 and send me the codes, and also make a clean report and send.

Problem 8.1

Brewer’s malt is produced from germinating barley, so brewers like to know under what conditions they should germinate their barley. The following is part of an experiment on barley germination. Barley seeds were divided into 30 lots of 100 seeds, and each lot of 100 seeds was germinated under one of ten conditions chosen at random. The conditions are the ten combinations of weeks after harvest (1, 3, 6, 9, or 12 weeks) and amount of water used in germination (4 ml or 8 ml). The response is the number of seeds germinating. We are interested in whether timing and/or amount of water affect germination. The data for this problem are in Table 8.1 (Hareland and Madson 1989). Analyze these data to determine how the germination rate depends on the treatments.

Problem 8.2 (For this problem, no calculation, no R, simple tell me whats wrong with this design)

Particleboard is made from wood chips and resins. An experiment is conducted to study the effect of using slash chips (waste wood chips) along with standard chips. The researchers make eighteen boards by varying the target density (42 or 48 lb/ft3 ), the amount of resin (6, 9, or 12%), and the fraction of slash (0, 25, or 50%). The response is the actual density of the boards produced (lb/ft3 , data from Boehner 1975). Analyze these data to determine the effects of the factors on particleboard density and how the density differs from target.

Problem 8.4

Implantable heart pacemakers contain small circuit boards called substrates. These substrates are assembled, cut to shape, and fired. Some of the substrates will separate, or delaminate, making them useless. The purpose of this experiment was to study the effects of three factors on the rate of delamination. The factors were A: firing profile time, 8 versus 13 hours with the theory suggesting 13 hours is better; B: furnace airflow, low versus high, with theory suggesting high is better; and C: laser, old versus new, with theory suggesting new cutting lasers are better. A large number of raw, assembled substrates are divided into sixteen groups. These sixteen groups are assigned at random to the eight factorlevel combinations of the three factors, two groups per combination. The substrates are then processed, and the response is the fraction of substrates that delaminate. Data from Todd Kerkow

Analyze these data to determine how the treatments affect delamination.

HLT 362V Chapter 8 Factorial Treatment Structure and R Code Worksheet
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HLT 362V Chapter 8 Factorial Treatment Structure and R Code Worksheet
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HLT 362V Chapter 8 Factorial Treatment Structure and R Code Worksheet
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Discussion: Factorial Treatment Structure

Discussion: Factorial Treatment Structure

You must proofread your paper. But do not strictly rely on your computer’s spell-checker and grammar-checker; failure to do so indicates a lack of effort on your part and you can expect your grade to suffer accordingly. Papers with numerous misspelled words and grammatical mistakes will be penalized. Read over your paper – in silence and then aloud – before handing it in and make corrections as necessary. Often it is advantageous to have a friend proofread your paper for obvious errors. Handwritten corrections are preferable to uncorrected mistakes.

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